For any translator, a computer is a pipeline for making their living. The keyboard is our working tool. Studying the keyboard carefully is a key to workflow efficiency.

After two decades of consistent typing, I finally decided to keep my operations with the mouse to an absolute minimum. While I do not have any signs of the tunnel syndrome currently, I feel I should care better about my wrists.

My aim was to make close friends with my keyboard—a totally regular device—and learn key shortcuts for most of the commands I use.

Below, you’ll find a list of hotkeys and keyboard combinations worth learning complemented with a downloadable table for reference and editing. Obviously, for each of us the list will differ depending on favourite devices, operational systems, programmes, and combinations you already know. Key combinations for Windows (Windows 7 to be exact) are followed by Mac variants in brackets.

The following links will provide you with full lists of keyword combinations for Windows 7, Windows 10 and macOS. Keep studying them and choose new combinations to master regularly. Attach small sticky notes to the outlines of your computer’s screen with 3 or 4 shortcuts you want to master next. Then, leave it all to your fingers.

1. Keyboard

Start with studying ALL the keys including the <Windows> key, the <Menu> context key (to the left of the right <Ctrl>, with a cursor and window icon) and the right <Alt> key (AltGr).

(On keyboards made for Macs, you generally use the <Option> key instead of <Alt> and <Command> instead of the Windows logo key.)

Pay attention to the command line in Windows. Here are some programs and tools you can quickly launch with the help of the command line (<Win + R>):

calc: Run Calculatorcontrol: Run Control Panelexcel: Run MS Excelmspaint: Run Microsoft Paintnotepad: Run Notepadtaskmgr: Run Task Managershutdown/s: Turn off the computer in 30 secondsshutdown/h: Hibernate the computershutdown/r: Restart the computerwinword: Run MS Word

You won’t need to type a command every time as the system will memorize it and autosuggest options.

Tip: When you enter a URL into the command line, the system opens it after launching the browser.

1. Windows / Mac programs and tools

You don’t need to ignore your mouse. Try to combine it with the keyboard for generous time savings.

Shift: Block CD-ROM autorun (press after inserting the CD)
Shift+F10: Show the content menu for the selected item
Alt+PrtSc: Make a screenshot of an active window
Alt+F4: Close the active item (or exit the active program)
Ctrl+F4: Close the active document

MAC

Command-Tab: Switch to the next most recently used app among your open apps
Shift-Command-N: Create a new folder in the Finder
Shift-Command-4: Make a screen shot of an active window
Command-Comma (,): Open preferences for the front app

Keyword combinations for the Windows Task Panel:

Shift + click on the icon in the task panel: Open a new application window
Shift + right click: Show the application menu
Ctrl + click on the group of icons: Unfold all their windows

Tip: If after opening the Explorer you press a letter key, the system highlights the file (or the folder) with the name starting with that letter.

2. Text Editors

These application are among the basic tools for translators. By continuing to study hotkeys and key combinations for handling text documents we increase our efficiency.

The <F8> in Windows is one handy key:

pressed twice, it selects the word around (or touching) the cursor

pressed three times it selects the whole sentence

pressed four times it selects the whole paragraph

pressed five times, it selects the whole document.

Position the cursor where you wish to begin, press <F8> and then use the cursors to extend the selection. To escape the selecting mode, press the <Esc> key.

Shift + F5: Move to the last change
Ctrl + Alt + Z: Switch between the last four edits
Ctrl + Left/Right arrow (Option-Left/Right arrow): Move by the words
Ctrl + Up/Down arrow: Move by paragraphs or vertical scroll
Ctrl + Page Up/Page Down (Fn-Up/Down arrow): Scroll up or down one page
Home / End (Command-Left/Right arrow): Go to the start or the end of a line
The same with the Shift key pressed for Windows and Mac: Text selection

Make and print a table with special symbols to have them at hand. For Windows, you can include:
Copyright: Alt + Ctrl + C
Registered trade mark: Alt + Ctrl + R
Trade mark: Alt + Ctrl + T

Tip: To find the key combinations you need, open the list of special symbols in Microsoft Word. Select the symbol you need and look at the key combination in the lower right part of the window.

3. Browsers

This is another important application type for translators, with their own key combinations and ‘secrets’.

Find and study key combinations for your preferred browser. Use <Ctrl + Tab> to switch between the open tabs. To move to a tab you need, use <Ctrl+window number> (Command-1 to Command-9 for Macs).

Tip. The address bar works also as a search window. Type in the searched term and press <Enter>: you’ll see the results of your default search engine. To move the cursor to the address bar, use <Ctrl + L> (Command-L).

Windows + Chrome

Browser tabs:
Ctrl + N: New window
Ctrl + T: New tab
Ctrl + W: Close current tab
Ctrl + Shift + T: Reopen the last tab or window you closedBrowser pages:
Home: To the top of the page
End: To the bottom of the page
Ctrl + N: Bookmark the current page
Ctrl + H: History
Ctrl + J: Downloads

Mac + Safari

Browser tabs
Command-T: Open new tab
Command-W: Close Tab
Shift-Command-: Show all tabs
Shift-Command-T: Reopen the last tab or window you closed
Command – Shift – ]/[: Navigate between multiple tabsBrowser pages
Command-Up: To the top of the page
Command-Down: To the bottom of the page
Control-Command-1: Show or hide the Bookmarks sidebar
Command-Shift-I: Open new message with the URL of a page
Command-Y: Open/close the History window

4. CAT Tools

They were made for efficiency but we can always increase it a bit further. Some key combinations are the same as with operating systems. Others are application unique.

I’ve created an editable Word file with main key combinations for Windows mentioned above: follow the link to get main hotkeys for translators. Feel free to download it and use for learning hotkeys. You can replace the hotkeys you know well with some new combinations to master.

CAT tools have already been on the market for many years now and yet they are still improving. New technologies and emerging needs from translators are triggering a shift from computer-aided translation tools to smart device-aided translations tools. Does the future of productivity lie in web-based translation environments?

The emergence of online translation environments

While CAT tools nowadays are inevitable in the toolkit of translators, it is still not long ago that professional translators had to work without them. The tools for computer-aided translation, not to be confused with online translation tools like Google Translate, only emerged in the early 1990s. Although there might have been some earlier attempts to create software that helps translators to improve their quality, productivity and consistency, in the last decade of the last century they came into full swing. Nowadays translators can choose from at least 20 different CAT tools, both online and offline, to suit their needs out of which SDL Trados and MemoQ are by far the best known.
However, only 25 years after the introduction of mainstream translation software a new era is on the horizon. The introduction of cloud technology, the rise of digital nomads, and the general availability of cheap and fast internet connections has led to a new branch on the CAT tool tree: translators can now use online translation environments, both free and paid, to work wherever they choose to.

Translating online

The technological advancements in the last couple of years opened great opportunities for companies who looked beyond traditional CAT tools and wanted to pluck the low-hanging fruit of the cloud’s capabilities. Several professionals, both from inside and outside the translation industry, quickly introduced their own online variants of the desktop translation tools. Examples included Smartling and Memsource (which has a desktop tool as well). These tools are browser-based, which means that they are accessible as webpages and can be used to work wherever users want as long as they have a compatible device and an internet connection. The online translation environments offer full functionality, which is often equivalent to the standard desktop tools. Users (in the case of Smartling and Memsource mainly project managers) can create translation memories and term bases, set rules for quality assurance and require users to perform several checks before they can deliver their translations. The tools also offer support for the most common file formats, like Microsoft Office files, PDF files and HTML documents, but also for bilingual filetypes like XLIFF and the proprietary formats of Trados and MemoQ. In addition, they often have familiar user interfaces, with well-known toolbars and panels that make it easier for project managers and translators alike to find their way in the online CAT tool.

It might be clear that the new members in the CAT tool family are working disruptively to shake up the CAT tool industry. It is therefore not a surprise that after the introduction of new online CAT tools developers of ‘traditional’ CAT tools also came up with an online version. MemoQ introduced MemoQ Web while SDL brought SDL Online Translation Editor to the table.

Web-based CAT tools for translators

The most important feature of the web-based CAT tools is, (how surprising), that they work in a browser. Most of them were initially designed to work on a desktop, offering translators a convenient tool with omnipresent accessibility while at the same time making it easier for project managers to dispense projects. Indeed, project managers only had to upload files, create or connect a translation memory, and send a link to multiple translators, making it easier to complete projects, shorten the turnaround time, and circumvent lengthy discussions via email. But because these new online CAT tools were mainly directed at agencies and project managers, they fell short of meeting the needs of translators who wanted to work on the go. Other bright minds therefore developed new web-based CAT tools that supported the needs of the freelance translator better: in the past few years Lilt and Smartcat were introduced, among others. The SDL Online Translation Editor has also been created with freelance professionals in mind, while MemoQ Web is more dedicated to project managers.

The biggest difference between tools for freelance translators and project managers is their workflow. While project managers have loads of options to manage projects, tools like Lilt and Smartcat introduce only the options freelancers need: they can upload a file in different file types, create or use a translation memory (term bases are often not supported), work their way through the file, and complete the job. The tools have a familiar and simple user interface, so translators do not need to look for advanced options, but often, powerful options are hidden under the bonnet, so they can really compete with their desktop equivalents.
Another major advantage of CAT tools in the cloud is that they frequently release new features quickly and respond to feature requests even faster, while traditional CAT tools often require months for implementing, testing, and introducing new features in a newly built (minor) version of their tool.

Another major difference is that many tools aimed at freelancers are free to use. They offer various plans for advanced users, often based on the amount of characters being translated, but there is only one free flavour, and it comes without many of the options that paid users have access to.

Privacy concerns with online CAT environments

In the past few years the online CAT tools have quickly risen to the level at which they can compete with traditional computer-based CAT tools. Where CAT tools have evolved and added new features with every new release, their online counterparts were introduced according to the status quo of traditional CAT tools. They sometimes even introduced ground-breaking new features that traditional CAT tools were not able to offer, like Lilt’s adaptive machine translation.
Yet among translators there is still much debate about their adaptations. The most important concern is that of privacy. While computer resources are generally considered a safe option, many translators are afraid to use cloud environments because of the risk of hacks and leaks that expose clients’ confidential information. At the same time, using a free online translation environment sometimes requires that translations are shared with the platform provider to improve the quality of generally available translation memories and machine translation services. Freelancers, whose business depends on credibility, simply cannot afford to share their client’s information for the sake of improving their productivity or flexibility.
On the other hand, early adopters and technology enthusiasts debate that the cloud is much safer than many computers thanks to continuous security updates. However, they are only a small group in the world of translators.

From CAT to SAT?

Whatever the privacy concerns, until now the introduction of online CAT tools has made clear that they are here to stay. With the increasing adaptation of online tools, lifestyles shifting to working on the go, and digital nomadism it is expected that online translation environments will be increasingly in demand in the future.
Although traditional CAT tools do not offer any opportunities to be run on smart devices with an Android, iOS, or Windows Phone operating system, online CAT tools do not have this problem. That means that they can be used without barriers on smartphones and tablets, once they have been adopted on a computer. Indeed they offer the same experience everywhere as they are browser-based and do not need to be adapted much to work in different operating environments. An added advantage of this possibility is that users can start a task on their desktop, then work on it while away, and complete it in a third environment.

Yet, despite the seemingly endless possibilities of the online CAT tools, many of them still do not offer a flawless experience on smartphones and tablets. One of the biggest disadvantages of the browser-based tools is that they do not fit neatly onto the small screens of smart devices. A short experiment with a few translation platforms (Smartcat and Lilt; SDL’s Online Translator Editor returned an error) quickly showed that the user interface has problems with touch-enabled devices. While all elements of a CAT tool (the panel with the bilingual format, a panel with translation memory results, a concordance panel, and some other interface elements) are present, they often do not fit neatly. While the interface appears fine in its initial state, touching a text box to add a translation will cause the panels to be re-arranged every time. Furthermore after touching the screen the screen keyboard pops up, often making (a part of) the source text invisible. While this problem is apparent on tablets, it is even more problematic on smartphones with even smaller screens. Working on a translation on the go using a tablet of smartphone therefore does not offer a seamless, flawless, or productive experience just yet.

Another problem is that rendering the translation environment on a tablet or smartphone requires considerable computing resources on some devices. So in order to make full use of an online CAT tool, users need to have a powerful tablet or smartphone that can execute scripts and render style sheets quickly to realize a productivity gain.

That brings us to the question of whether online CAT tools can fulfil the needs of professional translators. Basically, the answer is yes. Online CAT tools often work well on desktops. However, they are currently an online variant on computer-aided translation tools. That does not mean that they are fully fledged to become smart device-based translation tools (SAT). The current generation of browser-based CAT tools is perfect to use with laptops while one is on the go, but in order to benefit from their full potential for smartphones and tablets they still need to be more adapted to these devices. The future of CAT tools is in our hands, but it still need to be adapted to our fingers.

Here’s the basic scenario: you have the translated versions of your documents, but the translation wasn’t performed in a CAT tool and you have to build a translation memory because these documents need to be updated or changed across the languages, you want to retain the existing elements, style and terminology, and you have integrated CAT technology in your processes in the meantime. The solution is a neat piece of language engineering called translation alignment.

Translation alignment is a native feature of most productivity tools for computer-assisted translation, but its application in real life is limited to very specific situations, so even the language professionals rarely have an opportunity to use it. However, these situations do happen once in while and when they do, alignment usually comes as a trusty solution for process optimization. We will take a look at two actual cases to show you what exactly it does.

Example No. 1: A simple case

Project outline:

Three Word documents previously translated to one language, totaling 6000 unweighted words. Two new documents totaling around 2500 words that feature certain elements of the existing files and need to follow the existing style and terminology.

Project execution:

Since the translated documents were properly formatted and there were no layout issues, the alignment process was completed almost instantly. The software was able to segmentize the source files and we matched the translated segments, with some minor tweaking of segmentation. We then built a translation memory from those matched segments and added the new files to the project.

The result:

Thanks to the created translation assets, the final wordcount of the new content was around 1500 and our linguists were able to produce translation in accordance with the previously established style and terminology. The assets were preserved for use on future projects.

Example No.2: An extreme case of multilingual alignment

Project outline:

In one of our projects we had to develop translation assets in four language pairs, totaling roughly 30k words per language. The source materials were expanded with new content totaling about 20k words unweighted and the language assets had to be developed both to retain the existing style and terminology solution and to help the client switch to a new CAT platform.

Project execution:

Unfortunately, there was no workaround for ploughing through dozens of files, but once we organized the materials we could proceed to the alignment phase. Since these files were localized and some parts were even transcreated to match the target cultures, which also included changes in layout and differences in content, we knew that alignment was not going to be fully automated.

This is why native linguists in these languages performed the translation alignment and communicated with the client and the content producer during this phase. While this slowed the process a bit, it ultimately yielded the best results possible.

We then exported the created translation memory in the cross-platform TMXformat that allowed use in different CAT tools and the alignment phase was finished.

The result:

With the TM applied, the weighted volume of new content was around 7k words. Our linguists localized the new materials in accordance with the existing conventions in the new CAT platform and the translation assets were saved for future use.

Wrap up

In both cases, translation alignment enabled us to reduce the volume of the new content for translation and localization and ensure stylistic and lexical consistencywith the previously translated materials. It also provided an additional, real-time quality control and helped our linguists produce a better translation in less time.

Translation alignment is not an everyday operation, but it is good to know that when it is called to deliver the goods, this is exactly what it does.

oão Graça, co-founder and CTO of Unbabel, on what machine translation can teach us about the challenges still lying ahead for artificial intelligence.

Can you understand this sentence? Now try understanding the long and convoluted and unexpectedly – maybe never-ending, or maybe ending-sooner-than-you-think, but let’s hope it ends soon – nature of this alternative sentence.

The complexities of language can be an inconvenience to a reader. But even to today’s smartest machine learning algorithms, there are more translation challenges remaining than advances in other fields would have you believe.

These challenges in particular are a good demonstration of the multitude of complexities that still remain for machines to catch up with human performance.

You say tomato

When it comes to translation, there are two categories of content. On one hand, you have “commodity” translation. Perhaps you want to point your phone at a menu and get a rough idea of what it is. Or you want to impress a colleague with a phrase from their local language.

Here, phrases are short, the content is often formal and errors aren’t life or death.

But on the other hand, you have interactions where context is key – understanding the intent of the writer or speaker, and the expectations of the reader or listener. Take any example where a business speaks to its customers – you better hope you are speaking their language respectfully when they have a complaint or problem.

It’s not enough to solve the problem at a superficial level, and to achieve comparably “human quality” communication still has an enormous amount of research ahead of it. This need for perfection is why most research is focused in this second area.

In the examples below, I discuss the challenges still ahead for the translation industry, and touch on what they mean for how we use machine learning tech more broadly.

Challenge 1: Long-distance lookups

Many of the biggest challenges are structural.

A good example is long distance lookups. If you are translating a sentence word by word, but the order is the same, it’s just solving “what is the correct equivalent of this for that?”

But once you start having to think about reordering the sentence, the problem space that has to be explored is exponentially larger. And in languages like Chinese and Japanese, you find verbs at the end of the sentence, potentially producing the longest distances possible.

The system needs to assess at least three reordering systems. This is why these languages are so hard, because you have to cater to very different grammatical patterns, very different vocabularies, and how many characters are in each word.

Here, you can see how expanding problem spaces create difficulties in an area the human brain handles with ease.

Challenge 2: Taxonomy

The second major area of complexity involves different formats of data.

For example, conversational language has a completely different structure and appropriate models than formal documents. In areas like customer service translation, this makes a big difference. Nobody likes to feel like the representative of a company is being overly officious when handling their problem.

Therefore, any model that is able to learn from a volume of real human queries will have an advantage — and doubly so if it’s able to take it from a particular industry sector. Meanwhile, other models might be relying on news stories or generic online text, and output completely different results.

Similarly, with other machine learning challenges, the ability to learn from the most valuable and representative data can give a big advantage – or risk limiting taxonomical flexibility.

This brings us to context.

Challenge 3: Context

Most translation models still translate sentence by sentence, so they don’t take the context into account.

If they are translating a pronoun, they have no clue which pronoun should be translated. They will randomly generate sentences that are formal or informal. They don’t guarantee consistency of terminology – for instance, translating a legal term correctly in the same way throughout. There’s no way you can guarantee the whole document is correct.

The other problem is the content is not always in the same language. Sometimes it’s one sentence in Chinese, one sentence in English. The sentences are much shorter, so you probably have to look much higher for context. This reaches its extreme in “chat” interactions.

And the context problem is different than if you were translating an email. For example, if you are doing a legal document and the document is ten pages long, you would need to use the entire document for an accurate contextual translation.

This is next to impossible with current models – you have to find some way to summarise it. Otherwise, consistency is nearly impossible.

On the other hand, if you are translating for something like SEO, what you are actually translating is key words that don’t form a sentence, just keywords by themselves. This means you turn to more dictionary-like translation to disambiguate and use other words or the image associated with it.

People think “Oh, we are in the age of unlimited data” but actually we are still enormously lacking in many ways.

Yes, we have a lot of data but often not enough relevant data.

Looking to the future

There will be many translation engines but what makes them different is their models.

The model is going to look at the data and predict patterns and assign them to different customers, and from then, will decide which voice/ language/ tone/ etc. to choose.

In current common public translation tools, they aren’t aware of this yet. They don’t even have the knowledge of the document from where the translation came from, let alone the speaker or their translation preferences.

This will bring in the next level of sophistication in this area. Machine learning, exercised against use-specific corpus of language, will give fast and accurate translations, while being able to forward them to humans to finalise and learn from further.

Languages might still drive machines crazy – but with careful human thinking, we can teach them to persevere.

The idea that robots are taking over human jobs is by no means a new one. Over the last century, the automation of tasks has done everything from making a farmer’s job easier with tractors to replacing the need for cashiers with self-serve kiosks. More recently, as machines are getting smarter, discussion has shifted to the topic of robots taking over more skilled positions, namely that of a translator.

A simple search on the question-and-answer site Quora reveals dozens of inquiries on this very issue. While a recent survey shows that AI experts predict that robots will take over the task of translating languages by 2024. Everyone wants to know if they’ll be replaced by a machine and more importantly, when will that happen?

“I’m not worried about it happening in my lifetime” translator, Lizajoy Morales, told me when I asked if she was afraid of losing her job to a machine. This same sentiment echoes with most of Lilt’s users. Of course, this demographic is already using artificial intelligence to their advantage and tend to see the benefits over than the drawbacks.

Many translators, however, are quick to argue that certain types of content are impossible to be translated accurately by a machine, such as literature, which relies on a human’s understanding of nuance to capture the author’s intention. Or in fields like legal or medicine, that rely on the accuracy of a human translator.

But even in these highly-specialized fields, machines can find their place in the translation workflow. Not as a replacement, but rather as an assistant. As translators, we can use machines to our advantage, to work better and faster.

But I’m not talking about post-editing of machine translation. In a recent article from a colleague, Greg Rosner talks of the comparison of post-editing to the job of a janitor — just cleaning up a mess. True machine assistance augments the translator’s existing abilities and knowledge, letting them have the freedom to do what they do best — translate — and keeping interference to a minimum.

So how do machines help translators exactly? With an interactive, adaptive machine translation, such as that found in Lilt, the system learns in real-time from human feedback and/or existing translation memory data. This means that as a translator is working, the machine is getting to know their content, style and preferences and thus adapting to this unique translator/content combination. This adaptation allows the system to progressively provide better suggestions to human translators, and higher quality for fully automatic translation. In basic terms, it’s making translators faster and better.

Morales also pointed out another little-known benefit from machine translation suggestions: an increase in creativity. “This is an unexpected and much-appreciated benefit. I do all kinds of translations, from tourism, wine, gastronomy, history, social sciences, financial, legal, technical, marketing, gray literature, even poetry on occasion. And Lilt gives me fantastic and creative suggestions. They don’t always work, of course, but every so often the suggestion is absolutely better than anything I could have come up with on my own without spending precious minutes searching through the thesaurus…once again, saving me time and effort.”

Many are also finding that with increased productivity, comes increased free time. Ever wish there were more hours in the day? If you’re a translator, machine assistance may be the solution.

David Creuze, a freelance translator, told us how he spends his extra time, “I have two young children, and to be able to compress my work time from 6 or 7 hours (a normal day before their birth) to 4 hours a day, without sacrificing quality, is awesome.”

With these types of benefits at our fingertips, we should stop worrying about machines taking the jobs of translators and focus on using the machine to our advantage, to work better and ultimately focus on what we do best: being human.

Research at Facebook just made it easier to translate between languages without many translation examples. For example, from Urdu to English.

Neural Machine Translation

Neural Machine Translation (NMT) is the field concerned with using AI to translate between any language such as English and French. In 2015 researchers at the Montreal Institute of Learning Algorithms, developed new AI techniques [1] which allowed machine-generated translations to finally work. Almost overnight, systems like Google Translate became orders of magnitude better.

While that leap was significant, it still required having sentence pairs in both languages, for example, “I like to eat” (English) and “me gusta comer” (Spanish). For translations between languages like Urdu and English without many of these pairs, translation systems failed miserably. Since then, researchers have been building systems that can translate without sentence pairings, ie: Unsupervised Neural Machine Translation (UNMT).

In the past year, researchers at Facebook, NYU, University of the Basque Country and Sorbonne Universites, made dramatic advancements which are finally enabling systems to translate without knowing that “house” means “casa” in Spanish.

Just a few days ago, Facebook AI Research (FAIR), published a paper [2] showing a dramatic improvement which allowed translations from languages like Urdu to English. “To give some idea of the level of advancement, an improvement of 1 BLEU point (a common metric for judging the accuracy of MT) is considered a remarkable achievement in this field; our methods showed an improvement of more than 10 BLEU points.”

Let us know what do you think about this new leap!

Though machine translation has been around for decades, the most you’ll read about it is the perceived proximity to the mythical “Babel Fish” –an instantaneous personal translation device– itself ready to replace each and every human translator. The part that gets left out is machine translation’s relationship with human translators. For a long time, this relationship was no more complex than post-editing badly translated text, a process most translators find to be a tiresome chore. With the advent of neural machine translation, however, machine translation is not just something that creates more tedious work for translators. It is now a partner to them, making them faster and their output more accurate.

So What’s the Big Deal?

Before we jump into the brave new translating world of tomorrow, let’s put the technology in context. Prior to neural machine translation, there have been two main paradigms in the history of the field. The first was rules-based machine translation (RBMT) and the second, dominant until very recently, was phrase-based statistical machine translation (SMT).

When building rules-based machine translation systems, linguists and computer scientists joined forces to write thousands of rules for translating text from one language to another. This was good enough for monolingual reviewers to be able to get the general idea of important documents in an otherwise unmanageable body of content in a language they couldn’t read. But for the purposes of actually creating good translations, this approach has obvious flaws: it’s time consuming and, naturally, results in low quality translations.

Phrase-based SMT, on the other hand, looks at a large body of bilingual text and creates a statistical model of probable translations. The trouble with SMT is its reliance on systems. For instance, it is unable to associate synonyms or derivatives of a single word, requiring the use of a supplemental system responsible for morphology. It also requires a language model to ensure fluency, but this is limited to a given word’s immediate surroundings. SMT is therefore prone to grammatical errors, and relatively inflexible when it encounters phrases that are different from those included in its training data.

Finally, here we are at the advent of neural machine translation. Virtually all NMT systems use what is known as “attentional encoder-decoder” architecture. The system has two main neural networks, one that receives a sentence (the encoder) and transforms it into a series of coordinates, or “vectors”. A decoder neural network then gets to work transforming those vectors back into text in another language, with an attention mechanism sitting in between, helping the decoder network focus on the important parts of the encoder output.

The effect of this encoding is that an NMT system learns the similarity between words and phrases, grouping them together in space, whereas an SMT system just sees a bunch of unrelated words that are more or less likely to be present in a translation.

Interestingly, this architecture is what makes Google’s “zero-shot translation” possible. A well-trained multilingual NMT can decode the same encoded vector into different languages it knows, regardless of whether that particular source/target language combination was used in training.

As the decoder makes its way through the translation, it predicts words based on the entire sentence up to that point, which means it produces entire coherent sentences, unlike SMT. Unfortunately, this also means that any flaws appearing early in the sentence tend to snowball, dragging down the quality of the result. Some NMT models also struggle with words it doesn’t know, which tend to be rare words or proper nouns.

Despite its flaws, NMT represents a huge improvement in MT quality, and the flaws it does have happen to present opportunities.

Translators and Machine Translation: Together at Last

While improvements to MT typically mean increases in its usual applications (i.e. post-editing, automatic translation), the real winner with NMT is translators. This is particularly true when a translator is able to use it in real time as they translate, as opposed to post-editing MT output. When the translator actively works with an NMT engine to create a translation, they are able to build and learn from each other, the engine offering up a translation the human may not have considered, and the human serving as a moderator, and in so doing, a teacher of the engine.

For example, during the translation process, when the translator corrects the beginning of a sentence, it improves the system’s chances getting the rest of the translation right. Often all it takes is a nudge at the beginning of a sentence to fix the rest, and the snowball of mistakes unravels.

Meanwhile, NMT’s characteristic improvements in grammar and coherence mean that when it reaches a correct translation, the translator spends less time fixing grammar, beating MT output and skipping post-editing all together. When they have the opportunity to work together, translators and their NMT engines quite literally finish each other’s sentences. Besides speeding up the process, and here I’m speaking as a translator, it’s honestly a rewarding experience.

Where Do We Go Now?

Predicting the future is always a risky business, but provided the quality and accessibility of NMT continues to improve, it will gradually come to be an indispensable part of a translator’s toolbox, just as CAT tools and translation memory already have.

A lot of current research has to do with getting better data, and with building systems that need less data. Both of these areas will continue to improve MT quality and accelerate its usefulness to translators. Hopefully this usefulness will also reach more languages, especially ones with less data available for training. Once that happens, translators in those languages could get through more and more text, gradually improving the availability of quality text both for the public and for further MT training, in turn allowing those translators, having already built the groundwork, to move on to bigger challenges.

When done right, NMT has the potential to not just improve translators’ jobs, but to move the entire translation industry closer to its goal of being humanity’s Babel Fish. Not found in an app, or in an earbud, but in networks of people.